A deep learning-powered web app that detects craters in images! Using a custom-trained YOLOv8 model on the OHRC dataset, this project aims to make crater detection accessible and simple through an interactive web interface.
- Custom YOLOv8 Model: Specifically trained to detect craters using high-resolution OHRC satellite imagery.
- Web-Based Interface: Easily upload your own images to get instant crater detection results!
- Quick and Intuitive: Get real-time crater analysis with minimal setup.
Try it yourself!
- Run the web app (instructions below).
- Upload an image, and watch as craters are highlighted instantly!
- Python: Version 3.7 or higher.
- Pip: Ensure pip is installed to install the required packages.
-
Clone the Project:
git clone https://github.com/yourusername/Crater_Detection.git cd Crater_Detection -
Install Dependencies:
pip install -r requirements.txt
-
Launch the Web App:
python webapp.py
-
Access the App: Open your browser and go to http://127.0.0.1:5000 to start detecting craters in your images!
Here's what you'll find in this repository:
webapp.py: The main app file. This is where the magic happens! It launches a Flask server to handle user uploads and run crater detection on your images.yolo_model/: Directory where the YOLOv8 model files are stored.requirements.txt: List of dependencies needed to run the app.templates/: HTML templates for the web interface, giving it a clean and user-friendly layout.
Our YOLOv8 model has been finely tuned on the OHRC (Optical High-Resolution Camera) dataset, a powerful set of high-quality satellite imagery specifically used for crater detection. By leveraging YOLO's real-time object detection capabilities, this model is able to spot craters with impressive accuracy.
The model was trained with custom parameters to maximize crater detection accuracy, optimized specifically for the OHRC dataset. Check out training.md (if available) for more information on the training process and dataset preparation.
Want to try it on your own machine? Here’s how!
-
Clone the Repository:
git clone https://github.com/yourusername/Crater_Detection.git cd Crater_Detection -
Install Requirements:
pip install -r requirements.txt
-
Run the App:
python webapp.py
-
Open in Browser: Once you see the link in your terminal (
http://127.0.0.1:5000), open it in your browser to upload an image and detect craters on it!
- Use Clear Images: Crater detection works best with clear, high-resolution images.
- Zoom In for Accuracy: Larger craters are more likely to be detected accurately.
- Experiment with Different Angles: Satellite imagery from different angles can yield interesting results.
We welcome contributions! If you’d like to contribute, feel free to fork the repo, make your changes, and submit a pull request. Contributions to improve the model, web app UI, or add new features are all welcome.
This project is licensed under the MIT License.
-
Clone the Repository:
git clone https://github.com/yourusername/Crater_Detection.git cd Crater_Detection -
Install Requirements:
pip install -r requirements.txt
-
Run the Web App:
python webapp.py
-
Open in Browser: Visit http://127.0.0.1:5000 to upload and analyze your images!
Happy Crater Detecting! 🌌🌑